報告人：南京大學 姜正瑞 教授
時間：12月17日 上午10:00-11:30
會議號：騰訊會議159 894 820
內容摘要：
In the presence of successive product generations, most consumers are repeat buyers, who may decide to purchase a future product generation before its release. Therefore, after a new product generation enters the market, its sales often exhibit a declining pattern, thus rendering traditional diffusion models unsuitable for characterizing consumers’ upgrade timing decisions. In this study, we propose an Exponential-Decay Proportional Hazard Model (Expo-Decay model) to predict consumers’ time to product upgrade. The Expo-Decay model is parsimonious, interpretable, and performs better than existing models. We apply the Expo-Decay model and three extensions to study consumers’ upgrade behaviors for a sports video game series. Empirical results reveal that consumers’ previous adoption and usage patterns can help predict their timing to upgrades. In particular, we find that consumers who have adopted the immediate past generation and those who play previous generations more often tend to upgrade earlier, whereas those who specialize in a small subset of game modes tend to upgrade later. Further, we find that complex extensions to the Expo-Decay model do not lead to better prediction performance than the baseline Expo-Decay model, while a time-variant extension that updates the values of covariates over time outperforms the baseline model with static data.
報告人簡介：
姜正瑞教授是南京大學商學院營銷與電子商務系教授，二級教授，博士生導師。在2019年加入南大之前，任美國愛荷華州立大學的信息系統與商業分析教授和托米講席教授。主要研究領域是商務智能與大數據分析，其研究特色是將計算機科學研究與管理學研究有效融合，在商業數據分析、機器學習、決策支持和科技創新擴散等方向做出了重要貢獻，在國際頂級期刊如 Management Science , MIS Quarterly , Information Systems Research , IEEE Transactions on Knowledge and Data Engineering 等發表十多篇論文。其研究成果還被應用于企業、非盈利組織和政府的實踐，取得了客觀的經濟效益。現為國際頂尖期刊 Information Systems Research 的副主編和 Production and Operations Management 的高級主編；曾任另一頂級期刊 MIS Quarterly 的副主編，并獲得該刊2016年最佳副主編獎。主持過信息系統領域多個國際性的學術會議。作為項目主持人曾收到國家自然科學基金和美國國際開發署的資助，在北美、中國和非洲從事過科研和知識傳播的工作。2019年被南京市委市政府授予“南京市高層次舉薦人才（A類）”的榮譽稱號。
